A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network

2019 ◽  
Vol 29 (2) ◽  
pp. 723-737 ◽  
Author(s):  
Yonghui Shang ◽  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Quang-Hieu Tran ◽  
Hossein Moayedi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Firefly Algorithm ◽  
Ground Vibration ◽  
Open Pit ◽  
Open Pit Mines ◽  
Artificial Neural ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine

2020 ◽  
Vol 29 (6) ◽  
pp. 4121-4132 ◽  
Author(s):  
Parichehr Bayat ◽  
Masoud Monjezi ◽  
Mojtaba Rezakhah ◽  
Danial Jahed Armaghani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Firefly Algorithm ◽  
Ground Vibration ◽  
Artificial Neural

scholarly journals Optimization of Artificial Neural Network for Stock Market Price Prediction Using an Enhanced Firefly Algorithm

2019 ◽  
Vol 13 (9) ◽  
pp. 532-543
Author(s):  
Ameen Ahmed Oloduowo ◽  
Fashoto Stephen Gbenga ◽  
Ogeh Clement ◽  
Balogun Abdullateef ◽  
Mashwama Petros
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Firefly Algorithm ◽  
Market Price ◽  
Price Prediction ◽  
Artificial Neural

Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Tamer Moussa ◽  
Salaheldin Elkatatny ◽  
Mohamed Mahmoud ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Well Log ◽  
Well Test ◽  
Log Data ◽  
Hydrocarbon Reservoir ◽  
Reservoir Permeability ◽  
Artificial Neural ◽  
Core Permeability

Permeability is a key parameter related to any hydrocarbon reservoir characterization. Moreover, many petroleum engineering problems cannot be precisely answered without having accurate permeability value. Core analysis and well test techniques are the conventional methods to determine permeability. These methods are time-consuming and very expensive. Therefore, many researches have been introduced to identify the relationship between core permeability and well log data using artificial neural network (ANN). The objective of this research is to develop a new empirical correlation that can be used to determine the reservoir permeability of oil wells from well log data, namely, deep resistivity (RT), bulk density (RHOB), microspherical focused resistivity (RSFL), neutron porosity (NPHI), and gamma ray (GR). A self-adaptive differential evolution integrated with artificial neural network (SaDE-ANN) approach and evolutionary algorithm-based symbolic regression (EASR) techniques were used to develop the correlations based on 743 actual core permeability measurements and well log data. The obtained results showed that the developed correlations using SaDE-ANN models can be used to predict the reservoir permeability from well log data with a high accuracy (the mean square error (MSE) was 0.0638 and the correlation coefficient (CC) was 0.98). SaDE-ANN approach is more accurate than the EASR. The introduced technique and empirical correlations will assist the petroleum engineers to calculate the reservoir permeability as a function of the well log data. This is the first time to implement and apply SaDE-ANN approaches to estimate reservoir permeability from well log data (RSFL, RT, NPHI, RHOB, and GR). Therefore, it is a step forward to eliminate the required lab measurements for core permeability and discover the capabilities of optimization and artificial intelligence models as well as their application in permeability determination. Outcomes of this study could help petroleum engineers to have better understanding of reservoir performance when lab data are not available.

Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm

2018 ◽  
Vol 35 (4) ◽  
pp. 1774-1787 ◽  
Author(s):  
Katayoun Behzadafshar ◽  
Fahimeh Mohebbi ◽  
Mehran Soltani Tehrani ◽  
Mahdi Hasanipanah ◽  
Omid Tabrizi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Vibration ◽  
Imperialist Competitive Algorithm ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Content Type ◽  
Competitive Algorithm ◽  
Spacing Ratio ◽  
Artificial Neural

PurposeThe purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran.Design/methodology/approachFor this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models’ input, and the peak particle velocity (PPV) parameter was used as the models’ output.FindingsAfter modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with theR2 of 0.939 was the most precise model for predicting the PPV in the present study.Originality/valueIn the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

Business viability of potential software projects using artificial neural network

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
O. Okolo ◽  
B.Y Baha
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Project Selection ◽  
Business Value ◽  
Synthetic Approach ◽  
Software Project ◽  
Unseen Data ◽  
Artificial Neural

Selection of a software project is a critical decision. This selection involves prediction to ascertain a project that provides the best business value to the organization. The process of selection is carefully undertaken to optimize scarce resources available, which makes it impossible to simultaneously invest in all business ideas and systems. The current traditional method of software selection does not consider risk factors among the many variables necessary to predict a project that could provide the best business value. More so, the current method such as an artificial intelligence approach, where project managers use more robust models to make predictions have not received the needed attention in developing models for software project selection. This research applied a branch of Artificial Intelligence called Artificial Neural Network to classify projects into three levels. The research designed an artificial neural network of four inputs, one hidden layer with twenty-seven (27) neurons, and three outputs. Keras, a python deep learning library that runs on a theano background was used to implement the model. This research used a secondary dataset, which was enhanced by the synthetic approach, to make the required data features needed in machine learning applications. Backpropagation Algorithm enabled the model to train and learn from the data, and K-fold cross-validation was used to measure the accuracy of the model on unseen data. The results of the simulation showed that the model performed up to 98.67% accuracy with a standard deviation of 2.6% performance on unseen data. The research concludes that using the artificial neural network for software project selection unleashes a new vista of opportunities in artificial i ntelligence where intelligent systems are developed based on robust models from data forproject selection.Keywords: Artificial Neural Network, Project selection, Machine LearningVol. 26, No. 1, June 2019

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